Method and apparatus for determining the cerebral state of a patient with fast response

ABSTRACT

A method and apparatus for ascertaining the cerebral state of a patient. The method/apparatus may find use in ascertaining the depth of anesthesia of the patient. In one embodiment, the entropy of the patient&#39;s EEG signal data is determined as an indication of the cerebral state. A frequency domain power spectrum quantity is obtained from the patient&#39;s EMG signal data. The latter quantity can be updated more frequently than the EEG entropy due to its higher frequency. The EEG entropy indication and the EMG power spectrum indication can be combined into a composite indicator that provides an immediate indication of changes in the cerebral state of the patient. In another embodiment, the frequency range over which the entropy of the biopotential signal from the patient is determined is broadened to encompass both EEG signal data and EMG signal data and the entropy so determined used as an indication of the patient&#39;s cerebral state.

BACKGROUND OF THE INVENTION

[0001] The present invention relates to a method and apparatus fordetermining the cerebral state of a patient. One application of themethod and apparatus is determining the extent of a hypnotic state ofthe patient resulting, for example, from the administration of ananesthetic agent. That extent is often termed the “depth of anesthesia.”In the method and apparatus of the present invention, changes in thecerebral state can be accurately and quickly determined.

[0002] In a simplistic definition, anesthesia is an artificially inducedstate of partial or total loss of sensation or pain, i.e. analgesia. Formost medical procedures the loss of sensation is accompanied by a lossof consciousness on the part of a patient so that the patient isamnestic and is not aware of the procedure.

[0003] The “depth of anesthesia” generally describes the extent to whichconsciousness is lost following administration of an anesthetic agent.As the magnitude of anesthetization, or depth of anesthesia, increases,an anesthetized patient typically fails to successively respond tospoken commands, loses the eyelid reflex, loses other reflexes,undergoes depression of vital signs, and the like.

[0004] While loss of consciousness (hypnosis, amnesia) and the loss ofsensation (analgesia) are significant features of anesthesia, it shouldbe noted that balanced high quality anesthesia must also consider musclerelaxation, suppression of the autonomous nervous system, and blockadeof the neuro muscular junction. Sufficient muscle relaxation is requiredto ensure optimal operating conditions for the surgeon manipulating thepatient's tissue. The autonomous nervous system, if not suppressed,causes the patient to respond to surgical activity with a shock reactionthat effects heavily on hemodynamics and the endocrine system. To keepthe patient completely motionless, the neuro muscular junctionstransmitting orders from the brain to the muscles of the body need to beblocked so that the body of the patient becomes completely paralyzed.

[0005] While the need to determine the state of all five components ofanesthesia is widely recognized, ascertaining and quantifying the stateof hypnosis or depth of anesthesia in a reliable, accurate, and quickmanner has been, and is, the subject of extensive attention. One reasonfor this is its importance. If the anesthesia is not sufficiently deep,the patient may maintain or gain consciousness during a surgery, orother medical procedure, resulting in an extremely traumatic experiencefor the patient, anesthesiologist, and surgeon. On the other hand,excessively deep anesthesia reflects an unnecessary consumption ofanesthetic agents, most of which are expensive. Anesthesia that is toodeep requires increased medical supervision during the surgery recoveryprocess and prolongs the period required for the patient to becomecompletely free of the effects of the anesthetic agent. A second reasonfor the continuing study and attention being given to monitoring thehypnotic condition of a patient arises because of its difficulty: thatis, anesthetic agents alter the activity and state of the patient'sbrain and these changes are not always easy to detect.

[0006] A measure of the depth of anesthesia that may be used forresearch purposes is found in an Observer's Assessment of Alertness andSedation or OAAS. The OAAS determines the level of consciousness or,conversely, the depth of sedation or anesthesia, based on a patient'sresponse to external stimuli. One such assessment that classifies thedepth of anesthesia in six levels, is summarized by the table below. Thetransition from consciousness to unconsciousness may be deemed to occurwhen the OAAS score changes from level 3 to level 2. Level zerocorresponds to a state of deep anesthesia in which the patient shows noresponse to a very painful stimulus. OAAS Score DistinctiveCharacteristics 5 Patient replies readily to spoken commands, eyes open,awake. 4 Patient is sedated, but replies to spoken commands, mildptosis. 3 Patient ceases to reply to loud commands, eye lid reflectpresent. 2 Patient does not reply to spoken commands, no eye lid reflex.1 Patient does not react to TOF stimulation (50 mA) with movement. 0Patient does not react to tetanic stimulation with movement.

[0007] “Ptosis” is a drooping of the upper eyelids. “TOF stimulation”(“train-of-four”) is a very short, painful electrical (50 mA) stimulusapplied to the ulnar nerve in the arm of the patient, repeated fourtimes to evaluate the intensity of muscular contraction. In “tetanicstimulation” the electrical current (50 mA) is applied continuously fora period of time, such as 5 seconds. The ulnar nerve is the nerve which,when pinched, gives rise to the well known “crazy or funny bone” effect.

[0008] While useful for research and other purposes, an OAAS scaleprovides only a limited number of scaling levels and is limited inpractical use because of the attention required from theanesthesiologist and the use of painful stimuli.

[0009] It has long been known that the neurological activity of thebrain is reflected in biopotentials available on the surface of thebrain and on the scalp. Thus, efforts to quantify the extent ofanesthesia induced hypnosis have turned to a study of thesebiopotentials. The biopotential electrical signals are usually obtainedby a pair, or plurality of pairs, of electrodes placed on the patient'sscalp at locations designated by a recognized protocol and a set, or aplurality of sets or channels, of electrical signals are obtained fromthe electrodes. These signals are amplified and filtered. The recordedsignals comprise an electroencephalogram or EEG.

[0010] Among the purposes of filtering is to remove electromyographic(EMG) signals from the EEG signal. EMG signals result from muscleactivity of the patient and will appear in electroencephalographicelectrodes applied to the forehead or scalp of the patient. They areusually considered artifacts with respect to the EEG signals. Since EMGsignals characteristically have most of their energy in a frequencyrange (40-300 Hz) which is different than that of the EEG, majorportions of the EMG signals can be separated from the EEG signal.

[0011] A typical EEG is shown in FIG. 1. A macro characteristic of EEGsignal patterns is the existence of broadly defined low frequencyrhythms or waves occurring in certain frequency bands. Four such bandsare recognized: Delta (0.5-3.5 Hz), Theta (3.5-7.0 Hz), Alpha (7.0-13.0Hz) and Beta (13.0-32.0 Hz). Alpha waves are found during periods ofwakefulness and may disappear entirely during sleep. The higherfrequency Beta waves are recorded during periods of intense activationof the central nervous system. The lower frequency Theta and Delta wavesreflect drowsiness and periods of deep sleep.

[0012] By analogy to the depth of sleep, it can be said that thefrequency of the EEG will decrease as the depth of anesthesia increases,while the magnitude of the signal initially often increases. However,this gross characterization is too imprecise and unreliable to use as anindication of such a critical medical aspect as the extent of hypnosis.Further, EEG signal changes during anesthesia may not fully correlatewith changes in the hypnotic state of the patient. For example, it hasbeen reported that in a 12-18 Hz frequency band, EEG activity initiallyincreases as anesthetic agents are administered and only thereafterdecreases as anesthesia deepens.

[0013] The foregoing circumstance has led to the investigation and useof other techniques to study EEG waveforms to ascertain the underlyingcondition of the brain, including the depth of anesthesia to which apatient is subjected. It will be immediately appreciated from FIG. 1that EEG signals are highly random in nature. Unlike other biopotentialsignals, such as those of an electrocardiogram (ECG), an EEG normallyhas no obvious repetitive patterns, the morphology and timing of whichcan be conveniently compared and analyzed. Nor does the shape of the EEGwaveform correlate well to specific underlying events in the brain.Hence, except for certain phenomena, such as epileptic seizures, whichare readily apparent from visual inspection of an EEG, the indication ofother conditions in the brain in the EEG is much more subtle.

[0014] Prefatory to the use of other techniques, the EEG signals aresubjected to analog to digital signal conversion by sequentiallysampling the magnitude of the analog EEG signals and converting same toa series of digital data values. The sampling is typically carried outat a rate of 100 Hz or greater. The digital signals are stored in themagnetic or other storage medium of a computer and then subjected tofurther processing to ascertain the underlying state of the brain. Suchprocessing typically uses sets of sequential EEG signal samples or datapoints representing a finite block of time, commonly termed an “epoch.”The analysis of the data is usually carried out on a moving averagebasis employing a given epoch and a certain number of backward epochs.

[0015] Some of the techniques by which EEG signals can be analyzed in aneffort to determine the depth of anesthesia are well described in Ira J.Rampil, A Primer for EEG Signal Processing in Anesthesia, Vol. 89,Anesthesiology No. 4, pgs. 980 et seq., October 1998.

[0016] One such technique is to examine, in some meaningful way, how thevoltage of an EEG signal changes over time. Such an analysis is termed a“time-domain analysis.” Because of its generally random nature, an EEGsignal is not a deterministic signal. This means that it is not possibleto exactly predict future values of the EEG from past values in themanner that, for example, the shapes of past QRS complexes in an ECGsignal can be used to predict future values for analytical anddiagnostic purposes. However, certain statistical characteristics ofrandom signals, such as an EEG, can be determined and used for analyticpurposes.

[0017] Time-domain based EEG analysis methods have not proven greatlysuccessful in clinical applications since the results do not behave in acompletely consistent manner. However, such methods have been reportedin the use of an electrical power parameter derived from the time-domainEEG signal voltage to control administration of an anesthetic agent.Combinations of time-domain based statistic parameters have been used toanalyze EEG data. Efforts have also been made to use the number of timesthe EEG signal crosses the zero voltage level in a given period toanalyze EEG signal data.

[0018] Time-domain based analysis is however useful in the study andquantification of burst suppression in the EEG. During deep sleep oranesthesia, the EEG time-domain signal may develop a pattern of activitywhich is characterized by alternating periods or “bursts” of normal, orhigh frequency and amplitude, voltage signals and periods of low or novoltage, which periods are termed those of “suppression.” The extent ofthis phenomenon can be expressed as a “burst suppression ratio (BSR)”which is a time domain EEG parameter describing the time the EEG voltageis in the suppressed state as a fraction of the sampling period.

[0019] A second approach to analyzing EEG waveforms examines signalactivity as a function of frequency, i.e. a “frequency-domain analysis.”It has long been recognized that complex waveforms, such as EEG signals,can be decomposed, or transformed, into a plurality, or spectrum, ofsimple sine or cosine waves of various frequencies, amplitudes, andphases. Frequency-domain spectra can be obtained from sequentialtime-domain EEG signal data by a Fourier transform. Frequency-domainanalysis analyzes the spectrum of frequency signals obtained from thetransform to determine characteristics and features occurring in waveforms having the various frequencies of the spectrum. The results of anEEG frequency-domain analysis are typically graphically displayed as apower versus frequency histogram in which frequency is graphed on theabscissa and power is graphed on the ordinate.

[0020] Further efforts to obtain useful information fromelectroencephalograms have employed higher order analyses, including thebispectrum and trispectrum. The bispectrum, which measures thecorrelation of phase between two different frequency components andquantifies the relationships among the underlying sinusoidal componentsof the EEG, has received considerable attention. The bispectrumspecifically quantifies the relationship between sinusoids at twoprimary frequencies f₁ and f₂ and a modulation component at thefrequency f₁+f₂. A strong phase relationship between f₁, f₂ and f₁+f₂creates a large bispectral value for frequency f₁+f₂. However, becausethe calculation must be performed using complex number arithmetic forseveral thousand f₁, f₂, and f₁+f₂ frequency combinations, thecomputations to obtain bispectral information are rather arduous.

[0021] For clinical use, it is desirable to simplify the results of EEGsignal analysis of the foregoing, and other types, into a workableparameter that can be used by an anesthesiologist in a clinical settingwhen attending the patient. Ideally, what is desired is a simple, singleparameter or index that quantifies the depth of anesthesia on aconsistent, continuous scale extending from full alertness to maximallydeep, but reversible, hypnosis. To be fully useful such a scale shouldmaintain its consistency, notwithstanding the differing pharmacologicaleffects of different anesthetic agents, as well as the differingphysiologies of different patients.

[0022] Various such parameters for relating EEG signal data to thehypnotic state of the patient are discussed in the literature. Severaluse frequency domain power spectral analysis. These parameters includepeak power frequency (PPF), median power frequency (MPF), and spectraledge frequency (SEF). A peak power frequency (PPF) parameter uses thefrequency in a spectrum at which occurs the highest power in the sampleddata as an indication of the depth of anesthesia. The median powerfrequency (MPF) parameter, as its name implies, uses the frequency thatbisects the spectrum. In the same fashion, the spectral edge frequencyuses the highest frequency in the EEG signal. A modification of thelatter is the SEF 95 parameter, which is the frequency below which 95%of the power in the spectrum resides.

[0023] To improve the consistency of an indicator of the hypnotic stateor depth of anesthesia, several parameters are often employed incombination. For example, the spectral edge frequency (SEF) parametermay be combined with the time-domain burst suppression ratio (BSR)parameter to improve the consistency and accuracy with which the depthof anesthesia can be indicated.

[0024] While parameters of the foregoing types can detect changes in theEEG caused by anesthetic agents and hence are useful in determining thedepth of anesthesia, they suffer from an inability to be calibrated tobehavioral endpoints and because of their sensitivity to the differentEEG patterns induced by different anesthetic agents.

[0025] More complex combinations of parameters are described in U.S.Pat. Nos. 4,907,597; 5,010,891; 5,320,109; and 5,458,117 to NassibChamoun or Chamoun et al. and are employed in the anesthesia monitorproduct made and sold by the assignee of the patents, Aspect MedicalSystems of Framingham, Mass. The patents describe various combinationsof a time-domain subparameter and frequency-domain subparameters,including a high order spectral subparameter, to form a single variable,termed the bispectral index (BIS), that correlates behavioralassessments of sedation and hypnosis over a range of anesthesia forseveral anesthetic agents. Because of this ability, the Aspect MedicalSystems product has found clinical acceptance.

[0026] The bispectral index, BIS, consists of the following threesubcomponents: SyncFastSlow, BetaRatio, and Burst Suppression. Thecalculation of the subparameter SyncFastSlow utilizes bispectralanalysis in the frequency-domain. The SyncFastSlow parameter correspondsto the logarithm of the ratio of the sum of all bispectral peaks in thefrequency range 0.5-47 Hz divided by the sum in the range 40-47 Hz. Thebispectral information in the SyncFastSlow subparameter does not, byitself, give sufficient information over the range of hypnosis thusrequiring combination with the other subparameters. The BetaRatiosubparameter gives the logarithm of the power ratio in the frequencyranges 30-47 Hz and 11-20 Hz. It is a frequency-domain parameter thathas been found to work best in light sedation. As noted above, in verydeep levels of anesthesia, EEG signal contains data samples in which theEEG activity is suppressed. The Burst Suppression Ratio obtained from atime-domain analysis of the EEG signal describes the relative content ofburst and suppression in the signal. The Burst Suppression Ratio isoperative in deep anesthesia in which the suppression occurs.

[0027] The resulting bispectral index, BIS, is a combination of thesethree subparameters. The combining algorithm weights the differentsubparameters according to their range of best performance. While thedetails of the algorithm are unpublished and proprietary, it is knownthat different subparameters or combination of subparameters areemployed depending on the level of hypnosis or depth of anesthesia. Forexample, light sedation, it is necessary to use the bispectralSyncFastSlow subparameter in conjunction with the BetaRatio subparameterin order produce reliable results. For deep anesthesia it is necessaryto combine the bispectral subparameter SyncFastSlow with the BurstSuppression Ratio subparameter to produce reliable results. Thealgorithm appears circuitous in that in order to make the propercombination of subparameters required to accurately determine the depthof anesthesia, the algorithm must know what the level of anesthesia iswhich, in turn, requires the proper subparameter combination.

[0028] Certain paradoxical behavior of the bispectral index (BIS) hasbeen reported. See Detsch, et al. “Increasing Isoflurane Concentrationmay cause Paradoxical Increases in the EEG bispectral index in SurgicalPatients”, Br. J. Anaesth. 84 (2000), pgs. 33-37. Because the index usesa plurality of subparameters and combinations thereof in differentregions of hypnosis, this behavior may occur when the hypnotic level ofa patient is at a boundary of the regions, for example, in the rangebetween “surgical levels” and “deep hypnosis.”

[0029] Further, computation of the bispectral index (BIS) parameterrequires averaging several epochs of EEG data. Thus, this index may benot sufficiently fast to detect changes in the state of a patient as isrequired in the clinical situation. See, Baker, et al.Electroencephalographic Indices Related to Hypnosis and Amnesia DuringPropofol Anaesthesia for Cardioversion, Anaesthesia and Intensive Care,Vol. 28, No.4, 2000. Hence, the BIS index may indicate recovery severalseconds after a patient has already opened his/her eyes. This can be aserious problem in the use of the BIS index. By knowing the depth ofanesthesia, the anesthesiologist can more precisely control the amountof anesthetic agent administered to a patient. Often this results in areduction in the amount of agent administered. However, the lessenedamount of anesthetic agent increases the risk that the patient willawaken during surgery. It is therefore essential that ananesthesiologist knows immediately if a patient is approachingconsciousness out of the hypnotic state.

[0030] A different approach to the analysis of electroencephalographicsignals is to attempt to quantify the complexity of the highly randomEEG signal for use as an indication of the depth of anesthesia. Thisapproach is based on the premise that neuronal systems, such as those ofthe brain, have been shown to exhibit a variety of non-linear behaviorsso that measures based on the non-linear dynamics of the EEG signalshould allow direct insight into the state of the underlying brainactivity.

[0031] There are a number of concepts and analytical techniques directedto the complex nature of random and unpredictable signals. One suchconcept is entropy. Entropy, as a physical concept, describes the stateof disorder of a physical system. When used in signal analysis, entropyaddresses and describes the complexity, unpredictability, or randomnesscharacteristics of a signal. In a simple example, a signal in whichsequential values are alternately of one fixed magnitude and then ofanother fixed magnitude has an entropy of zero, i.e. the signal istotally predictable. A signal in which sequential values are generatedby a random number generator has greater complexity and a higherentropy.

[0032] Applying the concept of entropy to the brain, the premise is thatwhen a person is awake, the mind is full of activity and hence the stateof the brain is more non-linear, complex, and noise like. Since EEGsignals reflect the underlying state of brain activity, this isreflected in relatively more “randomness” or “complexity” in the EEGsignal data, or, conversely, in a low level of “order.” As a personfalls asleep or is anesthetized, the brain function begins to lessen andbecomes more orderly and regular. As the activity state of the brainchanges, this is reflected in the EEG signals by a relative lowering ofthe “randomness” or “complexity” of the EEG signal data, or conversely,increasing “order” in the signal data. When a person is awake, the EEGdata signals will have higher entropy and when the person is asleep theEEG signal data will have a lower entropy.

[0033] With respect to anesthesia, an increasing body of evidence showsthat EEG signal data contains more “order”, i.e. less “randomness”, andlower entropy, at higher concentrations of an anesthetic agent, i.e.greater depth of anesthesia, than at lower concentrations. At a lowerconcentration of anesthetic agent, the EEG signal has higher entropy.This is due, presumably, to lesser levels of brain activity in theformer state than in the latter state. See “Stochastic complexitymeasures for physiological signal analysis” by I. A. Rezek and S. J.Roberts in IEEE Transactions on Biomedical Engineering, Vol. 4, No. 9,September 1998 describing entropy measurement to a cut off frequency of25 Hz and Bruhn, et al. “Approximate Entropy as anElectroencephalographic Measure of Anesthetic Drug Effect duringDesflurane Anesthesia”, Anesthesiology, 92 (2000), pgs. 715-726describing entropy measurement in a frequency range of 0.5 to 32 Hz. Seealso H. Viertiö-Oja et al. “New method to determine depth of anesthesiafrom EEG measurement” in J. Clin. Monitoring and Comp. Vol. 16 (2000)pg. 16 which reports that the transition from consciousness tounconsciousness takes place at a universal critical value of entropywhich is independent of the patient.

[0034] The pertinence of the concept of entropy to the conscious andunconscious states of the brain is also supported in recent theoreticalwork (see Steyn-Ross et al., Phys. Rev. E60 1999, pgs. 7229-7311) whichapplies thermodynamic theory to the study of the brain. This work pointsto the conclusion that when a patient undergoing anesthetization passesfrom the conscious state to the unconscious state, a thermodynamic phasetransition of the neural system of the brain takes place which isroughly analogous to the phase change occurring when water freezes intoice. During the process of freezing, an amount of entropy, proportionalto the latent heat of the process is removed so that water and ice havedifferent entropies. The conscious and unconscious states of the brainmay therefore similarly be expected to have distinct, different valuesof entropy. The premise that loss of consciousness can be regarded asanalogous to a thermodynamic phase transition, lends further support tothe concept of entropy as a fundamental characteristic of the cerebralstate of the brain and to the use of entropy in determining depth ofanesthesia as employing a quantity reflecting the basic mechanisms ofthe brain rather than derived phenomena, such as power spectra,reflecting those mechanisms.

[0035] In sum, the following can be said. First, certain forms ofentropy have generally been found to behave consistently as a functionof anesthetic depth. See Bruhn et al. and H. E. Viertiö-Oja et al.“Entropy of EEG signal is a robust index for depth of hypnosis”,Anesthesiology 93 (2000) A, pg. 1369. This warrants consideration ofentropy as a natural and robust choice to characterize levels ofhypnosis. Also, because entropy correlates with depth of anesthesia atall levels of anesthesia, it avoids the need to combine varioussubparameters as in the bispectral index (BIS). Second, the transitionfrom consciousness to unconsciousness takes place at a critical level ofentropy which is independent of the patient. See Viertio-Oja et al. inJ. Clin. Monitoring and Computing. Thirdly, and of particular practicalsignificance, recovery of a patient toward consciousness from anesthesiacan often be predicted by a rise of entropy toward the critical level.

[0036] A number of techniques and associated algorithms are availablefor quantifying signal complexity, including those based on entropy, asdescribed in the Rezek and Roberts article in IEEE Transactions onBiomedical Engineering article. One such algorithm is that whichproduces spectral entropy for which the entropy values are computed infrequency space. Another algorithm provides approximate entropy which isderived from the Kolmogorov-Sinai entropy formula and computed inTaken's embedding space. See Steven M. Pincus, Igor M. Gladstone, andRichard A. Ehrenkranz, “A regularity statistic for medical dataanalysis”, J. Clin. Monitoring 7 (1991), pgs. 335-345. A program forcomputing approximate entropy is set out in the Bruhn et al., article inAnesthesiology. The spectral entropy and approximate entropy techniqueshave found use in analyzing the complexity of EEG signal data.

[0037] Another technique for non-linear analysis of highly randomsignals is expressed in Lempel-Ziv complexity in which the complexity ofa string of data points is given by the number of bytes needed to makethe shortest possible computer program which is able to generate thestring. See Abraham Lempel and Jacob Ziv, “On the complexity of finitesequences”, IEEE Trans., IT-22 (1976) pgs. 75-81.

[0038] A still further approach that may be applied to EEG signalanalysis is fractal spectrum analysis based on chaos theory. In fractalspectrum analysis, the EEG signal is divided into a harmonic componentand a fractal component. The harmonic component includes the simplefrequencies whereas the fractal component contains the part which isinvariant under scaling in time. It has been found that the fractalexponent Beta which corresponds to the frequency power law 1/f^(β)increases consistently in the course of deepening anesthesia.

BRIEF SUMMARY OF THE INVENTION

[0039] An object of the present invention is to provide an improvedmethod and apparatus for accurately determining the cerebral state of apatient, including the hypnotic or consciousness state of a patient andthe depth of anesthesia that a patient is experiencing.

[0040] A particular object of the present invention is to provide such amethod/apparatus that can rapidly make such determinations, especiallywhen a patient is emerging to the conscious state from unconsciousness.

[0041] The gist of the present invention is to combine an effectivemeasure of the cerebral state of a patient derived from EEG signal data,preferably a complexity measurement such as spectral entropy orapproximate entropy, with a more rapidly obtainable measure derived fromEMG signal data and to use the combination as a cerebral stateindication. When used as an indication of the hypnotic state, or depthof anesthesia, of the patient, the measure derived from the EMG signaldata enhances and confirms the determination of the hypnotic state madeusing the EEG signal data and renders ascertaining changes in thehypnotic state of the patient more rapid. This is of particularadvantage in alerting an attending anesthesiologist to the possibilitythat an anesthetized patient may shortly regain consciousness so thatthe anesthesiologist can take timely, appropriate action. The measurederived from the EMG signal data may comprise spectral power data.

[0042] Both the EEG and EMG signal data are typically obtained from thesame set of electrodes applied, for example, to the forehead of thepatient. The EEG signal component dominates the lower frequencies (up toabout 30 Hz) contained in the biopotentials existing in the electrodesand EMG signal component dominates the higher frequencies (about 50 Hzand above).

[0043] The presence of EMG signal data shows that the patient isconscious and thus can provide a rapid indication of theconscious-unconscious state of the patient. Importantly, because of thehigher frequency of the EMG data signal, the sampling time can besignificantly shorter than that required for the lower frequency EEGsignal data. This allows the EMG data to be computed more frequently sothat the overall diagnostic indicator can quickly indicate changes inthe state of the patient.

[0044] In one embodiment of the invention, the EEG signal data and theEMG signal data are separately analyzed and thereafter combined into adiagnostic index or indicator. As noted above, because of the celeritywith which changes in the anesthetic state of the patient can bedetermined from the EMG data, the overall index can quickly inform theanesthesiologist of changes in the state of the patient.

[0045] In another embodiment of the present invention, the spectralrange of the complexity computations, i.e. entropy computations, iswidened to extend into the EMG range. Thus, the spectral range overwhich the complexity computations are carried out to provide anindicator may extend from some lower frequency of, for example 0.5 to 7Hz, up to a frequency above 32 Hz. To filter out power lineinterference, the spectral range may be divided into bands with theelimination of frequencies around 50, 60 Hz and 100, 120 Hz. Forexample, in an embodiment in which the spectral range extends toapproximately 150 Hz, a lower frequency band (0.5-47 Hz) will containmostly EEG signal data while two upper bands (63-97 Hz and 123-147 Hz)will include primarily EMG activity. The use of a widened frequencyrange does not require a division of the spectrum into two segments ordoes the first embodiment because all components in the widenedfrequency range are treated in the same manner. And, any boundary withinthe spectral range would be artificial since the frequency bands for theEEG and EMG signal data are overlapping. The same analytical techniquesare thus used for all levels of hypnosis from conscious state down todeep anesthesia, the paradoxical behavior found with indicatorsemploying a plurality of subparameters and rules of combination forvarious levels of anesthesia is avoided.

[0046] Further, the complexity measurement obtained in this secondembodiment of the invention can be updated as often as is permitted bythe higher frequencies of the EMG signal data in the widened spectralrange of the complexity computation. This will provide a very currentindication to the anesthesiologist of the depth of anesthesia of thepatient.

[0047] The indicator obtained from the signal complexity computationover the widened spectral range can be used in conjunction with acomplexity measurement obtained only from the EEG portions of thefrequency spectrum to provide useful information to the anesthesiologistregarding what portion of the indicator comes from cerebral activity andwhat portion comes from muscle activity.

[0048] Various other features, objects, and advantages of the inventionwill be made apparent from the following detailed description and thedrawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

[0049]FIG. 1 shows an electroencephalogram.

[0050]FIGS. 2a and 2 b are graphs showing values of entropy as comparedto the conventional OAAS scale for a patient receiving an anestheticagent;

[0051]FIGS. 3a and 3 b are graphs showing values of entropy of a patientat surgical levels of anesthesia;

[0052]FIGS. 4a and 4 b are graphs showing values of entropy as comparedto the conventional OAAS scale for a patient emerging from anesthesia;

[0053]FIGS. 5a, 5 b, and 5 c are comparative graphic showings of varioustechniques for analyzing EEG signals;

[0054]FIG. 6 is a flow chart showing one embodiment of the presentinvention;

[0055]FIGS. 7a, 7 b, and 7 c are graphs showing OAAS levels, EEGentropy, and EMG amplitude, respectively;

[0056]FIG. 8 is a flow chart showing another embodiment of the presentinvention;

[0057]FIGS. 9a, 9 b, 9 c, and 9 d are graphs showing OAAS levels andcorresponding EEG and EMG based indicators of the depth of anesthesia;

[0058]FIG. 10 is a graph showing combined EEG and EMG entropy values;and

[0059]FIG. 11 shows apparatus for carrying out the present invention.

DETAILED DESCRIPTION OF THE INVENTION

[0060] In a preferred embodiment of the invention, relevant informationwith respect to depth of anesthesia is extracted from the EEG signaldata by computing a parameter that characterizes the amount of disorderor complexity in the signal. Suitable mathematical techniques include,for example, spectral entropy described in Rezek and Roberts,approximate Kolmogorov-Sinai entropy described in Pincus et al., andcomplexity described in Lempel-Ziv. The use of spectral entropy isdeemed advantageous because of its superior computational simplicity andis described below.

[0061] Computation of the spectral entropy of a signal according toRezek and Roberts includes four steps. The first is the power spectrumcalculation. The Fourier transform X(f_(i)) of the signal x(t_(i)) iscomputed by the fast Fourier transform technique (FFT). The powerspectrum P(f_(i)) is calculated by squaring the amplitudes of eachelement X(f_(i)) of the Fourier transform:

P(f _(i))=X(f _(i))*X*(f _(i))  (1)

[0062] where X*(f_(i)) is the complex conjugate of the Fourier componentX(f_(i)).

[0063] The power spectrum is then normalized. The normalized powerspectrum P_(n)(f_(i)) is computed by setting a normalization constantC_(n) so that the sum of the normalized power spectrum over the selectedfrequency region [f₁,f₂] is equal to one: $\begin{matrix}{{\sum\limits_{f_{i} = f_{1}}^{f_{2}}\quad {P_{n}\left( f_{i} \right)}} = {{C_{n}{\sum\limits_{f_{i} = f_{1}}^{f_{2}}\quad {P_{n}\left( f_{i} \right)}}} = 1}} & (2)\end{matrix}$

[0064] In the summation step, the unnormalized spectral entropycorresponding to the frequency range [f₁,f₂] is computed as a sum$\begin{matrix}{{S\left\lbrack {f_{1},f_{2}} \right\rbrack} = {\sum\limits_{f_{i} = f_{1}}^{f_{2}}{{P_{n}\left( f_{i} \right)}{\log \left( \frac{1}{P_{n}\left( f_{i} \right)} \right)}}}} & (3)\end{matrix}$

[0065] Thereafter, the entropy value is normalized. In order tonormalize the entropy value to range between 1 (maximum disorder) and 0(complete order), the value is finally divided by the factor log(N[f₁,f₂]) where N[f₁,f₂] is equal to the total number of frequencycomponents in the range [f₁,f₂]: $\begin{matrix}{{S_{N}\left\lbrack {f_{1},f_{2}} \right\rbrack} = \frac{S\left\lbrack {f_{1},f_{2}} \right\rbrack}{\log \left( {N{{f_{1},f_{2}}}} \right)}} & (4)\end{matrix}$

[0066] In the original work by Rezek and Roberts, the frequency rangeconsidered was the range below f₂=25 Hz, as it is generally assumed thatmost of the EEG activity is confined to the frequency band belowapproximately 30 Hz.

[0067]FIG. 2 shows, as a function of time, values of entropy as computedabove as compared to an OAAS scale for a patient receiving an anestheticagent.

[0068]FIG. 2a indicates the OAAS level as determined by ananesthesiologist attending the patient. As noted above, at an OAAS level5, the patient is fully awake whereas at the OAAS level 0 corresponds toa deep state of anesthesia in which the patient shows no response totetanic stimulation. Horizontal line 10 indicates a level at whichtransition from the conscious to unconscious state is deemed to takeplace, i.e. between OAAS level 3 and OAAS level 2.

[0069] In the example shown in FIG. 2, the attending anesthesiologistconsiders the patient to have moved from OAAS level 5 to OAAS level 4 atabout three minutes. At about four minutes, the patient is deemed tohave dropped to OAAS level 3.

[0070] Thereafter, at about four and a half minutes, the patient isdeemed to have lost consciousness as by failing to respond to verbalcommands and the loss of the eyelid reflex. This is evidenced in thechange from level 3 to below level 2 and the crossing of horizontal line10.

[0071]FIG. 2b shows a value of entropy computed from five seconds ofdata as graph 20 and a value of entropy computed as median values oftwelve sequential five second epochs (sixty seconds) of data as graph30. As can be seen from FIG. 2b, as the consciousness of the patientdecreases from the commencing of monitoring, both graphs 20 and 30similarly decrease and cross horizontal line 40 which identifies theentropy level that characterizes the transition from the conscious stateto the unconscious state.

[0072] In accordance with a protocol for the OAAS in use, theanesthesiologist commences the application of TOF stimulations todetermine the depth of anesthesia on the OAAS scale. In the case shownin FIG. 2, the stimulations cause the patient to regain consciousness atabout eight minutes.

[0073] It will be seen from FIG. 2 that graphs 20 and 30 follow, andprovide an accurate indication of, the state of consciousness of thepatient, as presented on the OAAS scale.

[0074]FIGS. 3a and 3 b show the values of entropy at surgical levels ofanesthesia, i.e. when the OAAS scale is zero as shown in FIG. 3a.Horizontal line 40 in FIG. 3 is the same as horizontal line 40 in FIG. 1and comprises the entropic value forming the borderline between theconscious and unconscious states.

[0075]FIGS. 4a and 4 b shows a rapid recovery of a patient from surgicallevels of anesthesia to consciousness. The rise in the values ofentropy, informs the anesthesiologist of the approaching recovery to theconscious state.

[0076] While the invention has been described as using spectral entropy,FIGS. 5a, 5 b, and 5 c illustrate an example of anesthesia induction andemergence showing the suitability of the complexity measurements ofapproximate entropy and Lempel-Ziv complexity as well as spectralentropy to determine the depth of anesthesia. Measurements made usingboth shorter and longer samples of signal data are shown.

[0077] Other techniques for analyzing the EEG signal data can also beused, if desired, such as higher order frequency domain analysisincluding the bispectrum and trispectrum, frequency domain powerspectral analysis, and combinations of analytical quantities, such asthe bispectral index (BIS).

[0078] Measurement of electromyographic (EMG) activity contained in thebiopotentials in the electrodes on the forehead, or other region of thescalp, of the patient can provide useful information concerning thestate of an anesthetized patient. As the level of anesthesia approachesinadequacy, a painful stimulus causes a contraction of the frontalismuscle (frowning) which can be detected as peaks in the EMG amplitude ofthe signal obtained from the electrodes applied to the forehead of thepatient. EMG activity exists as long as the muscles are not paralyzed.This reaction can often be observed substantially before the paineventually brings the patient to consciousness. EMG signal data can thusprovide an early warning sign for the anesthesiologist to increase thelevel of anesthetics in order to prevent consciousness and awarenessduring surgery.

[0079] Further, due to the high frequency range of the primary portionof the EMG activity above 40 Hz, a comparatively small time window issufficient for computations using these frequency components, so thatchanges in the EMG activity can be detected substantially faster thanchanges in the EEG signal. Specifically, most of the component in theEEG signal resulting from brain activity is contained in the frequencyrange below 30 Hz. In order to obtain a good estimate of this activityby any mathematical procedure, the length of the signal used forcomputations has to be sufficient. In practice, the lowest frequency ofthe band sets the size for the signal length. For example, for an EEGsignal band from 0.5 Hz to 32 Hz, a signal sample 60 seconds long isrequired to obtain at least 30 cycles for each component. This sets alower limit to the response time for assessing changes in patient brainactivity from the EEG signal data. That is, the frequency with which allcomponents of the EEG signal data indicator can be computed and fullyupdated is about once every 60 seconds. By contrast, an EMG signal datain a 63-97 Hz band requires only 0.5 seconds of data to obtain 30cycles. The EMG signal data can thus be fully updated every half second.Because it can be so quickly updated, the EMG signal data can thereforeprovide an early warning sign for the anesthesiologist to increase, forexample, the level of anesthetic agent administered to the patient inorder to prevent awareness during surgery.

[0080]FIG. 6 is a flow chart showing the steps for producing an improveddiagnostic indication using EEG signal data and more rapidly indicativeEMG signal data in accordance with one embodiment of the presentinvention. In step 100, the signal data corresponding to thebiopotentials appearing in the electrodes placed on the scalp of thepatient is obtained. In step 110, the signal data is subjected tospectral decomposition. This, may, for example, be carried out usingFourier analysis.

[0081] The spectra are then divided into those representing the lowfrequency portions of the measured signal, for example, less than 30-50Hz and those representing the high frequency portion of the measuredsignal for example, those representing frequencies of 50 Hz and above.

[0082] Thereafter, the EEG spectrum estimate is processed at step 120 tocompute a parameter indicative of the state of activity of the brain. Asnoted above, it is presently deemed preferable to use a computation ofentropy for this purpose. However, other quantifications such as fractalspectrum analysis, Lempel-Ziv complexity, or bispectral or multispectralanalyses, such as the bispectral index (BIS), can be used for thispurpose. The result of this computation is the provision of anindication of the state of activity of the brain at step 122.

[0083] As noted above, in order to obtain a good estimate of thisactivity by the mathematical analyses described herein, the length ofthe signal used for computations has to be sufficient. In practice, thelowest frequency of the band sets the lower limit for the signal length.In the case of the EEG indication generated at step 120, the lower limitfor the signal is approximately 60 seconds. This means the indicationcan only be completely updated by repeating steps 100, 110, 120, and 122every 60 seconds and sets a lower limit to the response time of the EEGindication for assessing the patient's cerebral state.

[0084] A power spectrum of the EMG signal is obtained in step 124, as byobtaining an amplitude spectrum and thereafter squaring the values ofthe amplitude spectrum to create a power spectrum. The EMG powerspectrum provides an indication of EMG activity in step 126.

[0085] Due to the high frequency range of the EMG activity, for example,above 30 Hz, a comparatively small time window, for example 0.5 seconds,is sufficient to compute the EMG amplitude. This means that changes inthe EMG activity can be detected and the indicator updated by repeatingsteps 100, 110, 124, and 126 substantially faster than changes in theEEG indicator, as shown graphically at steps 124, 124 a, 124 b, etc.

[0086] In the example used above, the EMG indicator can be completelyupdated at a repetition rate of every 0.5 seconds. For simplicity indata processing, the EEG indication will typically also be recomputedevery 0.5 seconds. However, since the EEG indicator requires 60 secondsof data, each computation 120, 120 a, 120 b, etc. will use 59.5 secondsof old EEG signal data and 0.5 seconds of new EEG signal data. Thus, thechanges in the cerebral state of the patient contained in the EEG signaldata will be reflected only more slowly in the indication produced insteps 120 and 122 than the changes contained in the EMG indication.

[0087] The EEG indicator and the EMG indicator are combined in adiagnostic indicator or index in step 128.

[0088] The indicators produced in steps 120, 122, 124, 126 and 128 maybe subjected to statistical treatment, such as averaging, if desired.

[0089] The combined indication provided by the diagnostic index of step128 thus provides both reliable information of the activity state of thebrain, such as the level of hypnosis or depth of anesthesia as directlyfound in the EEG signal data, while full advantage can be taken of therapidly obtainable information included in the EMG component of thesignal which is a more indirect indication of the cerebral state of thepatient but is particularly useful in alerting the anesthesiologist tothe emergence of a patient from anesthesia.

[0090] The components of the diagnostic indicator or index describedabove are shown in FIG. 7. An anesthetic agent is administered as abolus at time zero. The patient enters unconsciousness, as shown by anOAAS level below line 10 in FIG. 7a, thereafter emerges for a shortperiod of time, responsive to stimulation or the lack of furtheranesthetic agents, and is thereafter rendered unconscious. FIG. 7b showsthe entropy indication as obtained from steps 120 and 122 of FIG. 6.FIG. 7c shows the EMG amplitude obtained from steps 126 and 128 as aroot-mean-squared sum over the EMG range of the Fourier spectrum. Datafor five seconds are shown as the jagged lines. The smoother linesindicate one minute median filtered values.

[0091] The graph of entropy as it relates to the hypnotic state of thepatent resembles that of FIGS. 2 and 4. With respect to the EMGactivity, during the first two minutes following time zero, there isconsiderable EMG activity indicating that the patient is awake.Thereafter, the EMG activity decreases as the patient becomesunconscious. The unequivocal and immediate indication that the patienthas regained consciousness at the ten minute point given by the EMGamplitude is clearly apparent from FIG. 7c and will be reflected in thediagnostic indicator provided in step 128. This will advise theanesthesiologist that the patient is emerging from the anesthesia.

[0092] In another embodiment of the invention, incorporation of EMGsignal data into a diagnostic indicator or index can be obtained bywidening the frequency range of spectral entropy computations to onewhich extends from the EEG range into the EMG range thereby to includeboth EEG and EMG signal data. Although some small amount of EMG activitystarts from frequencies of 1 Hz or lower, most EMG activity istraditionally quantified in a higher frequency range, such as the rangefrom 40 Hz to 300 Hz (with notch filters at multiples of 50 Hz/60 Hz inorder to filter out the power line interference). However, there existsno clear frequency boundary for the EEG and EMG data signals and anintermediate frequency range between 30 Hz and 50 Hz contains both EEGand EMG components overlapping each other.

[0093] In providing an entropy based indicator obtained from a frequencyrange containing both EEG and EMG spectra, it is to be appreciated thatthis represents a departure from the customary expression of,particularly, EMG signal data. That is, as noted above in connectionwith the first embodiment of the present invention, EMG signalcharacteristics are customarily expressed as a voltage amplitude, forexample as a root mean squared spectral amplitude, and the amplitude ofthe voltage will vary as a result of variations in the EMG signal data.By contrast, EEG entropy is a dimensionless quantity which describes theamount of disorder in the signal. When viewed from the entropicstandpoint, the effect of EMG activity on the EEG signal is to createhigh frequency noise which increases the entropy of the combined signal.Entropy varies from 0 to 1, and the values are independent of theamplitude of the signal. The entropic expression of EEG signal data andthe amplitude expression of the EMG signal data thus present a formalincompatibility which is overcome in the present invention by the use ofentropy as a characteristics of both signal data.

[0094] Further, this embodiment of the invention represents a departurefrom the previous entropic treatment of EEG signal data which has beenlimited to the frequency range in which EEG predominates as comparedwith EMG. These frequency ranges are, for example, frequencies of 25 Hzand below (see Rezek et al.) or 32 Hz or below (see Bruhn et al.). Thepresent invention contemplates the use of frequencies in a rangeextending from some lower frequency, for example 0.5 Hz, to a higherfrequency which is in excess of 32 Hz.

[0095] The second embodiment of the invention is explained using afrequency range of 0.5 Hz to about 150 Hz. It is deemed preferable todivide the extended frequency range into three-bands: a 0.5-47 Hz band;a 63-97 Hz band; and a 123-147 Hz band. The range is divided into thethree bands at these frequencies in order to avoid the power lineharmonics at 50/100 Hz or 60/120 Hz depending on the frequency of thealternating current power mains. The lowest band contains most of theEEG components, while the two upper bands include primarily EMGactivity.

[0096]FIG. 8 is a flow chart showing the steps of producing an improveddiagnostic indication or index using a widened frequency range for thecomputation of spectral entropy in accordance with the second embodimentof the present invention. In step 200, the signal data corresponding tothe biopotential signals appearing in the electrodes placed on the scalpof the patient is obtained. In step 210, the signal data is subjected tospectral decomposition, as by using Fourier transformation. As notedabove, the spectral decomposition may be carried over a frequency rangeencompassing both the EEG signal data and the EMG signal data, forexample, approximately 0.5 Hz to approximately 150 Hz. Also as notedabove, the data is divided in spectral bands in order to omitfrequencies at those of the power mains.

[0097] At step 212, the lower frequency band of the EEG-EMG spectralrange as well as the higher frequency bands are processed to compute ameasure, such as spectral entropy, indicative of the complexity of theEEG and EMG signal data and state of activity of the brain and thefrontal muscles. A combined indicator or index is provided at step 214.As described above in connection with FIG. 6, because the EMG signaldata is available for complete updating more frequently than the EEGsignal data, updating the combined indicator at the repetition rate bywhich the EMG signal data parameter can be updated provides a rapidindication to the anesthesiologist of any changes in the hypnotic stateof the patient.

[0098] Variations of the signal amplitude due to variations in theelectrode contact properties etc. affect the EEG and EMG signal data inthe same proportion to each other, so the combined EEG-EMG entropyindicator is not affected by the signal amplitude.

[0099] Also, the approach taken in the second embodiment of theinvention is consistent with the fact that the EEG and EMG frequencybands are overlapping. Thus no artificial boundary for EEG and EMGregions has to be defined since all frequency components are treated inthe same way, i.e., a determination of the complexity properties.However, while the present invention features the use of a singlealgorithm for the range of conscious states of a patient, it will beappreciated the changes to the algorithm may be required, based onsignal to noise consideration.

[0100] A parameter containing only EEG entropic data from the lowfrequency band may be used as a separate indicator as shown in steps 216and 218. This can be used in connection with the EEG-EMG entropicindication to allow the anesthesiologist to determine what portion ofthe EEG-EMG entropic indicator comes from brain activity and whatportion comes from muscle activity. Thus while a clinicalanesthesiologist will probably find the combined EEG-EMG entropicindicator to be highly useful, a researcher may well find a comparisonof EEG signal data entropy and the combined EEG-EMG entropy obtainedover the wide frequency range to be of interest.

[0101] Similarly, a parameter containing the EMG entropic data from thehigher frequency bands may be used as a separate parameter by computingthe entropy at step 220 and providing the indicator at step 222. Thevarious diagnostic indicators are collectively shown at step 224 in FIG.8.

[0102] While both embodiments of the invention have been described aboveas utilizing Fourier transformation to decompose the spectrum of signalscontained in the electrode biopotentials, other transformations, such asvarious wavelet transformations, may be employed. In order to obtain thecorresponding indicators for pure EEG entropy, EEG-EMG entropy, and/orEMG entropy, the basis functions are divided into two classes, oneincluding the basis functions for EEG activity, and the other includingthe basis functions for EMG activity. After this classification, theindices for pure EEG entropy and EEG-EMG entropy are computed asdescribed above. It is plausible that for better separation of the EEGand EMG components, a set of basis functions including waveletsemulating the shapes of the EMG spikes may extract the EMG componentmore effectively.

[0103]FIG. 9 illustrates, as a function of time, the behavior of a pureEEG entropy parameter and the combined EEG-EMG entropy parameter alongwith the depth of anesthesia, as evaluated by an anesthesiologist on anOAAS scale. Also shown is EMG signal data as conventionally expressed asroot mean squared spectral amplitude. The jagged lines show valuescomputed from 5 seconds of data, while the smoother lines showone-minute median filtered values. An anesthetic agent is administeredas a bolus at time zero.

[0104] During the first two minutes, during which time the patient isawake, there is a lot of EMG activity present as can be seen from thegraph of FIG. 9d showing EMG amplitude. As a result of the high EMGactivity, the combined EEG-EMG entropy shown in FIG. 9b also showsrelatively high values, confirming that the patient is awake. At aboutthe two minute point the patient loses consciousness as shown in FIG. 9awhen the OAAS score falls below line 10. Simultaneously, the EMGactivity largely disappears as shown in FIG. 9d. The EEG-EMG entropyindicator and the EEG entropy indicator, produced at steps 218 and 226,respectively, of FIG. 8 follow each other down below line 40 demarcatingthe transition to unconsciousness indicating deepening hypnosis. SeeFIGS. 9b and 9 c.

[0105] At about 6-7 minutes both the entropy values of FIGS. 9b and 9 cshow that anesthesia starts to lighten. This is because no furtherhypnotics have been administered. At about ten minutes, the patientwakes up and significant EMG activity again emerges. The pure EEGentropy indicator show in FIG. 9c predicts and indicates the recovery,but the EMG effect in the combined EEG-EMG entropy indictor shown inFIG. 9b makes this more apparent in its crossing of line 40.

[0106] Thereafter following the ten minute point, patient issubsequently anesthetized again. The EMG activity gradually disappearsas shown in FIG. 9d and EMG-EEG entropy indicator reduces to a pure EEGentropy indicator, as can be seen by a comparison of FIGS. 9b and 9 c.

[0107] It is advantageous to scale the EEG-entropy parameter obtained instep 226 of FIG. 8 and the EEG-EMG entropy parameter obtained in step218 in such a way that they coincide exactly when the EMG activityceases completely, as this scaling allows for simultaneous graphicalrepresentation of the two pieces of information. This can be done in thefollowing way. Take the frequency range 0.5 to 32 Hz containing mainlyEEG signal data to be frequency range R₁. Take the frequency range 32 to147 Hz, with the power line frequencies removed, to be frequency rangeR₂ for mainly EMG signal data.

[0108] Perform for the frequency ranges [R₁]=[EEG] and[R₁]+[R₂]=[EEG]+[EMG] the steps 1-3 of the Rezek algorithm (Eqs.(1)-(3)) to obtain $\begin{matrix}{\quad {{{S\left\lbrack R_{1} \right\rbrack} = {\sum\limits_{R_{1}}{{P_{n}\left( f_{i} \right)}{\log \left( \frac{1}{P_{n}\left( f_{i} \right)} \right)}}}}{and}}} & (5) \\{\quad {{S\left\lbrack {R_{1} + R_{2}} \right\rbrack} = {\sum\limits_{R_{1} + R_{2}}{{P_{n}\left( f_{i} \right)}{\log \left( \frac{1}{P_{n}\left( f_{i} \right)} \right)}}}}} & (6)\end{matrix}$

[0109] Assume for a moment that P_(n)(f_(i))=0 for all f_(i) within therange [R₂]. In this case, the normalization constant C_(n)[R₁+R_(2]=C)_(n)[R₁] and, consequently, the normalized spectra P_(n)[R₁+R_(2] and P)_(n)[R₁] (step 2) are equal. It follows that the unnormalized spectralentropies give by Eqs. (5) and (6) are equal. If these entropy valuesare now normalized according to Eq. (4), they will no longer be equal,because the normalization factor log(N[R₁+R₂]) is obviously larger thanlog(N[R₁]). This situation can be corrected by redefining the normalizedentropy S_(N)[R₁] in the following way: $\begin{matrix}{{S_{N}\left\lbrack R_{1} \right\rbrack} = {{\frac{\log \left( {N\left\lbrack R_{1} \right\rbrack} \right)}{\log \left( {N\left\lbrack {R_{1} + R_{2}} \right\rbrack} \right)}\frac{S\left\lbrack R_{1} \right\rbrack}{\log \left( {N\left\lbrack R_{1} \right\rbrack} \right)}} = \frac{S\left\lbrack R_{1} \right\rbrack}{\log \left( {N\left\lbrack {R_{1} + R_{2}} \right\rbrack} \right)}}} & (7)\end{matrix}$

[0110] The normalized entropy S_(N)[R₁+R₂] can be defined as previouslystated in Eq. (4): $\begin{matrix}{{S_{N}\left\lbrack {R_{1} + R_{2}} \right\rbrack} = \frac{S\left\lbrack {R_{1} + R_{2}} \right\rbrack}{\log \left( {N\left\lbrack {R_{1} + R_{2}} \right\rbrack} \right)}} & (8)\end{matrix}$

[0111] According to these definitions, the EEG-EMG entropy varies from 0to 1, whereas the pure EEG entropy varies from 0 tolog(N[R₁])/log(N[R₁+R₂])<1. The two entropy values coincide when thereis no EMG activity so that P(f_(i))=0 for all f_(i) within the range[R₂]. When EMG activity is present, EEG-EMG entropy is larger than thepure EEG entropy.

[0112] In practice, a digital filter is commonly used in processing thesignal data obtained from the patient's biopotentials. Due to thecharacteristics of such a filter the computed entropy of a completelyrandom signal, i.e. white noise, is usually slightly less than 1. Forthis reason, the entropies may be multiplied by a constant value so asto maintain the above described normalization.

[0113] While the foregoing is described using two frequency ranges, R₁,R₂, it will be appreciated that these ideas can be straightforwardlygeneralized to the case in which the number of frequency ranges islarger than two.

[0114]FIG. 10 shows the resulting normalized EEG entropy S_(N)[R₁](thick curve) together with the normalized EEG-EMG entropy S_(N)[R₁+R₂](thin curve). When presented in this manner, the EEG entropy givesreliable information of the trend behavior of the patient's brainactivity, while the EEG-EMG entropy responds rapidly to fast changes.

[0115] Instead of the EEG entropy indicator and the EEG-EMG entropyindicator shown in FIG. 9, it is possible to compute the correspondingentropy indicator for EMG activity alone as shown in FIG. 8, steps 220,222, and to use this together with the pure EEG entropy indicatorobtained in steps 216, 218. However, some care must be taken withrespect to this approach. When EMG activity ceases due to relaxation ofthe muscles, some noise is left in the EMG range of the spectrum. Theentropy of the noise may be relatively high and give a falsely highvalue for the level of EMG activity. Therefore, when EMG activity isconsidered separately and the concept of entropy is used forcomputations, a noise level should be established below which the EMGsignal is considered to be zero.

[0116] Apparatus for carrying out the present invention is shown in FIG.11. Electrodes 300 are applied to the head of the patient in a desiredmanner. Preferably, at least some of the electrodes are applied to theforehead of the patient. At least one pair and usually a plurality ofpairs of electrodes are utilized. The biopotentials appearing in theelectrodes are received in conductors 302 and are collected into patientcable 304.

[0117] Cable 304 connects conductors 302 to protection circuit 306 whichis operative in the event the patient is subjected to electro-surgery orcardiac defibrillation. Electro-surgery employs alternating current atradio frequencies, typically between 300 and 3000 Hz to cut tissue andcauterize bleeding blood vessels. A defibrillator delivers a shortcurrent pulse to arrest arrhythmia in the heart muscle. Either of theseoccurrences will significantly affect the signals in conductors 302 and,particularly, the EEG portion of the signals is usually rejected forfurther use in determining the cerebral state of the patient.

[0118] The output of protection circuit 306 is amplified by amplifier308 and subjected to analog to digital conversion in analog/digitalconverter 310. Thereafter the signals are provided to bandpass filter atfilter 312 that removes noise and line frequency harmonics from thesignals. The output from bandpass filter 312 is connected to artifactdetector 314.

[0119] Artifact detector 314 detects artifacts arising fromelectrocardiac activity, and other sources. The output of artifactdetector 314 is connected to computational unit 316 which carries outthe steps of the methods described above and shown in FIGS. 6 and 8 andproduces an output of the type shown in FIGS. 3, 4, 5, 7, 9, and 10 indisplay 318. Or, the information may be presented in display 318 innumerical form. Display 318 may also display other physiological data,such as electrocardiographic data, breath rate, pulse, blood pressure,etc., obtained from other monitors.

[0120] Also, while artifact detector 314 is used to remove artifacts,the presence of artifacts can also be dealt with in the signalprocessing occurring in computational unit 316. For example, eyemovements have been found to create simultaneous spikes in both thelower frequency EEG signal data as well as in the higher frequency EMGsignal data. Sensing of the presence simultaneous spikes in both thesefrequency bands may be deemed to result from eye movements andparticularly EEG signal data containing such artifacts can be eliminatedfrom use in making the determination of the cerebral state of thepatient. The same is also true when excessive muscular activity andcorresponding large EMG signal data is present.

[0121] The invention has been described above in connection withcerebral states induced by the administration of an anesthetic agent.However, it will be appreciated that the method and apparatus may beused in connection with other physiological conditions which arereflected in EEG and EMG signal data obtained from a patient and withdrugs other than anesthetic agents. It is therefore recognized thatother equivalents, alternatives, and modifications aside from thoseexpressly stated, are possible and within the scope of the appendedclaims.

1. A method for ascertaining the cerebral state of a patient, includinga state resulting from the administration of a drug, said methodcomprising the steps of: (a) obtaining EEG signal data from the patient;(b) obtaining EMG signal data from the patient; (c) analyzing a sampleof sequential EEG signal data to obtain a first indication indicative ofthe cerebral state of the patient; (d) analyzing a sample of sequentialEMG signal data temporally related to the EEG signal data sample toobtain a second indication indicative of electromyographic activity inthe patient; and (e) producing a composite indication from the first andsecond indications obtained at steps (c) and (d) indicative of thecerebral state of the patient.
 2. A method for ascertaining the cerebralstate of a patient, including a state resulting from the administrationof a drug, said method rapidly indicating changes in such state andcomprising the steps of: (a) obtaining EEG signal data from the patient;(b) obtaining EMG signal data from the patient, the EMG signal databeing primarily of a higher frequency than the EEG signal data which isprimarily of lower frequency; (c) analyzing a sample of sequential EEGsignal data to obtain a first indication indicative of the cerebralstate of the patient, the length of a EEG signal data sample being suchas to provide an cerebral state indication of desired accuracy; (d)analyzing a sample of sequential EMG signal data temporally related tothe EEG signal data sample to obtain a second indication indicative ofelectromyographic activity in the patient, it being possible to use aEMG signal data sample of shorter length than that of the EEG signaldata sample due to the higher frequency of the EMG signal data; and (e)producing a composite indication of the cerebral state of the patientfrom the first and second indications obtained at steps (c) and (d)indicative of the cerebral state of the patient, which compositeindication can be updated at a repetition rate determined by the shortersample length of the EMG signal data to rapidly indicate changes in thecerebral state of the patient.
 3. The method according to claim 1 orclaim 2 wherein step (c) is further defined as obtaining a measure ofthe complexity of the EEG signal data as the first indication.
 4. Themethod according to claim 3 wherein step (c) is further defined asobtaining a measure of the entropy of the EEG signal data as the firstindication.
 5. The method according to claim 4 wherein step (c) isfurther defined as obtaining the spectral entropy of the EEG signal dataas the first indication.
 6. The method according to claim 4 wherein step(c) is further defined as obtaining the approximate entropy of the EEGsignal as the first indication.
 7. The method according to claim 3wherein step (c) is further defined as obtaining a Lempel-Ziv complexitymeasure of the EEG signal data as the first indication.
 8. A methodaccording to claim 3 wherein step (c) is further defined as obtainingthe first indication from fractal spectrum analysis.
 9. The methodaccording to claim 1 or claim 2 wherein step (c) is further defined asobtaining the first indication from higher order frequency domainanalysis including the bispectrum or trispectrum.
 10. The methodaccording to claims 1, 2 or 3 wherein step (c) is further defined asobtaining the first indication from frequency domain power spectralanalysis of the EEG signal data.
 11. The method according to claim 1 orclaim 2 wherein step (c) is further defined as obtaining the firstindication from a combination of analytical quantities obtained from theEEG signal data.
 12. The method according to claim 11 wherein step (c)is further defined as employing a bispectral index (BIS) of the EEGsignal data as the first indication.
 13. The method according to claim 1or claim 2 wherein prior to analyzing the samples in steps (c) and (d)the EEG and EMG signal data is subjected to spectral decomposition. 14.The method according to claim 13 further defined as carrying out thespectral decomposition by means of a Fourier transform.
 15. The methodaccording to claim 13 further defined as carrying out the spectraldecomposition by employing a set of basis functions other than a Fourierset of functions.
 16. The method according to claim 15 further definedas carrying out the spectral decomposition by employing a set of basicfunctions corresponding to wavelet transformation.
 17. The methodaccording to claims 1, 2 or 3 wherein step (d) is further defined asobtaining the second indication from a frequency domain power spectrumof the EMG signal data.
 18. The method according to claim 3 wherein step(d) is further defined as obtaining a measure of the complexity of theEMG signal data as the second indication.
 19. The method according toclaim 18 wherein in step (d) a noise level is established below whichthe EMG signal data is considered to be zero.
 20. The method accordingto claim 1 or claim 2 further defined as repeating steps (c), (d), and(e) to update the composite indication.
 21. The method according toclaims 1, 2, 3, 4, or 5 further defined as one for ascertaining thehypnotic state of a patient.
 22. The method according to claims 17further defined as one for ascertaining the hypnotic state of a patient.23. The method according to claim 21 further defined as repeating steps(c), (d), and (e) to update the composite indication.
 24. The methodaccording to claim 17 further defined as repeating steps (c), (d), and(e) to update the composite indication.
 25. A method for ascertainingthe cerebral state of a patient, including a state resulting from theadministration of a drug, said method comprising the steps of: (a)obtaining biopotential signals from the patient, the biopotentialsignals containing EEG signal data and EMG signal data, the EMG signaldata being primarily of a higher frequency than the EEG signal datawhich is primarily of a lower frequency; (b) analyzing a sample ofsequential signal data over a frequency range that is sufficiently wideto include both the EEG and EMG signal data to obtain a measure of thecomplexity of the signal data; and (c) providing the measure as anindication of the cerebral state of the patient.
 26. A method forascertaining the cerebral state of a patient, including a stateresulting from the administration of a drug, said method rapidlyindicating changes in such state and comprising the steps of: (a)obtaining biopotential signals from the patient, the biopotentialsignals containing EEG signal data and EMG signal data, the EMG signaldata being primarily of a higher frequency than the EEG signal datawhich is primarily of lower frequency; (b) analyzing a sample ofsequential signal data over a frequency range that is sufficiently wideto include both the EEG and EMG signal data to obtain a measure of thecomplexity of the signal data, it being possible to use a EMG signaldata sample of shorter length than that of the EEG signal data sampledue to the higher frequency of the EMG signal data; and (c) providingthe measure as an indication of the cerebral state of the patient, whichindication can be updated at a repetition rate determined by the shortersample length of the EMG signal data to rapidly indicate changes in thecerebral state of the patient.
 27. The method according to claim 25 orclaim 26 wherein step (b) is further defined as analyzing thebiopotential signals over a frequency range extending from a frequencyof about 0.5 Hz to a frequency which is above 32 Hz.
 28. The methodaccording to claim 25 or claim 26 wherein step (b) is further defined asobtaining a measure of the entropy of the signal data.
 29. The methodaccording to claim 27 wherein step (b) is further defined as obtaining ameasure of the entropy of the signal data.
 30. The method according toclaim 28 wherein step (b) is further defined as obtaining the spectralentropy of the signal data.
 31. The method according to claim 28 whereinstep (b) is further defined as obtaining the approximate entropy of thesignal data.
 32. The method according to claim 25 or claim 26 whereinstep (b) is further defined as obtaining a Lempel-Ziv complexity measureof the signal data.
 33. A method according to claim 25 or claim 26wherein step (b) is further defined as obtaining the complexity measurefrom fractal spectrum analysis.
 34. The method according to claim 25 orclaim 26 wherein prior to analyzing the sample in step (b) thebiopotential signal is subjected to spectral decomposition.
 35. Themethod according to claim 34 further defined as carrying out thespectral decomposition by means of a Fourier transform.
 36. The methodaccording to claim 34 further defined as carrying out the spectraldecomposition by employing a set of basis functions other than a Fourierset of functions.
 37. The method according to claim 36 further definedas carrying out the spectral decomposition by employing a set of basicfunctions corresponding to wavelet transformation.
 38. The methodaccording to claim 25 or claim 26 further defined as repeating steps(c), (d), and (e) to update the indication.
 39. The method according toclaim 25 or claim 26 further defined as one for ascertaining thehypnotic state of a patient.
 40. The method according to claim 27further defined as one for ascertaining the hypnotic state of a patient.41. The method according to claim 28 further defined as one forascertaining the hypnotic state of a patient.
 42. The method accordingto claim 29 further defined as one for ascertaining the hypnotic stateof a patient.
 43. The method according to claim 39 further defined asrepeating steps (c), (d), and (e) to update the composite indication.44. The method according to claim 25 or claim 26 further defined asincluding the steps of analyzing a sample of the EEG signal data toobtain a complexity measure of the EEG signal data and providing thecomplexity measure of the EEG signal data as a further indication of thecerebral state of the patient.
 45. The method according to claim 44further defined as normalizing the further indication obtained from theanalysis of the EEG signal data and the EEG-EMG signal data indicationso that the further indication and EEG-EMG indication are equal in theabsence of EMG signal data.
 46. The method according to claim 45 whereinthe normalizing is carried out by multiplying the complexity measure ofthe EEG signal data by a quantity comprising the logarithm of the numberof frequency components used for computations for the EEG signal datacomplexity measure divided by the logarithm of the number of frequencycomponents used for the computations for the combined EEG-EMG complexitymeasure.
 47. The method according to claim 45 further defined asapplying a constant to the normalized indications to maintain thenormalization.
 48. The method according to claim 1 or claim 2 whereinstep (a) is further defined as obtaining the EEG signal data in afrequency range of approximately 0.5-32 Hz.
 49. The method according toclaim 1 or claim 2 wherein step (b) is further defined as obtaining EMGsignal data in a range of approximately 32-300 Hz.
 50. The methodaccording to claim 49 further defined as notch filtering the EMG signaldata to remove power line frequency harmonics.
 51. The method accordingto claim 25 or claim 26 further defined as notch filtering to removepower line frequency harmonics.
 52. The method according to claim 1, 2,25, or 26 wherein the EEG signal data and the EMG signal data areobtained from a common signal source.
 53. The method according to claim52 wherein the EEG and EMG signal data are obtained from biopotentialelectrodes applied to the head of the patient.
 54. The method accordingto claim 53 wherein at least the EMG signal data is obtained fromelectrodes applied to the forehead of the patient.
 55. The methodaccording to claim 1 or claim 2 further defined as processing the EEGand EMG signal data to detect artifacts.
 56. The method according toclaim 25 or 26 further defined as processing the biopotential signals orsignal data to detect artifacts.
 57. The method according to claim 55further defined as filtering the signal data to remove artifacts. 58.The method according to claim 56 further defined as filtering the signaldata or biopotential signals to remove artifacts.
 59. The methodaccording to claim 55 further defined as preventing the use of signaldata affected by artifacts.
 60. The method according to claim 56 furtherdefined as preventing the use of signal data or biopotential signalsaffected by artifacts.
 61. The method according to claim 55 furtherdefined as detecting excessive muscle activity in the patient from theEMG signal data and preventing the use of EEG signal data affected bythe muscle activity.
 62. The method according to claim 56 furtherdefined as detecting excessive muscle activity in the patient from thebiopotential signals or signal data and preventing the use of EEG signaldata affected by the muscle activity.
 63. The method according to claim55 further defined as including the step of sensing the presence ofelectrical energy at electro surgical frequencies and as preventing theuse of signal data affected by such an artifact.
 64. The methodaccording to claim 56 further defined as including the step of sensingthe presence of electrical energy at electro surgical frequencies and aspreventing the use of biopotential signals or signal data affected bysuch an artifact.
 65. The method according to claim 55 further definedas detecting simultaneous spikes in both the EEG signal data and EMGsignal data as eye movement artifacts and preventing the use of signaldata affected by such artifacts.
 66. The method according to claim 56further defined as detecting simultaneous spikes in both the EEG signaldata and EMG signal data as eye movement artifacts and preventing theuse of biopotential signals or signal data affected by such artifacts.67. A method for ascertaining the depth of anesthesia of a patient, saidmethod rapidly indicating changes in the hypnotic state of the patientand comprising the steps of: (a) obtaining biopotential signals from thepatient, the biopotential signals containing EEG signal data and EMGsignal data, the EMG signal data being primarily of a higher frequencythan the EEG signal data which is primarily of lower frequency; (b)analyzing a sample of sequential signal data existing in a frequencyrange extending from a frequency of about 0.5 Hz to a frequency which isabove 32 Hz to obtain a measure of the complexity of the signal data, itbeing possible to use a EMG signal data sample of shorter length thanthat of the EEG signal data sample due to the higher frequency of theEMG signal data; (c) providing the complexity measure as a firstindication of the cerebral state of the patient, which indication can beupdated at a repetition rate determined by the shorter sample length ofthe EMG signal data to rapidly indicate changes in the cerebral state ofthe patient; (d) analyzing a sample of EEG signal data to obtain acomplexity measure of the EEG signal data; (e) providing the complexitymeasure of the EEG signal data as a second indication of the cerebralstate of the patient; and (f) normalizing the first indication andsecond indication so that the first indication and second indication areequal in the absence of EMG signal data.
 68. Apparatus for ascertainingthe cerebral state of a patient, including a state resulting from theadministration of a drug, said apparatus comprising: (a) means forobtaining biopotential signals from the patient, the biopotentialsignals containing EEG signal data and EMG signal data; (b) meansanalyzing a sample of sequential EEG signal data to obtain a firstindicator indicative of the cerebral state of the patient and analyzinga sample of sequential EMG signal data temporally related to the EEGsignal data sample to obtain a second indicator indicative ofelectromyographic activity in the patient; and (c) means producing acomposite indicator from the first and second indicators indicative ofthe cerebral state of the patient.
 69. The apparatus according to claim68 wherein said analyzing means is further defined as using a length ofan EEG signal data sample to determine the first indicator and using anEMG signal data sample of shorter length than the EEG signal data sampleto determine said second indicator, said analyzing means completelyupdating said second indicator more frequently than said firstindicator.
 70. The apparatus according to claim 68 or claim 69 whereinsaid analyzing means is further defined as obtaining a measure of thecomplexity of the EEG signal data as the first indicator.
 71. Theapparatus according to claim 70 wherein said analyzing means is furtherdefined as obtaining a measure of the entropy of the EEG signal data asthe first indicator.
 72. The apparatus according to claim 70 whereinsaid analyzing means is further defined as obtaining a Lempel-Zivcomplexity measure of the EEG signal data as the first indicator. 73.The apparatus according to claims 68, 69, or 70 wherein said analyzingmeans is further defined as obtaining the second indication from afrequency domain power spectrum of the EMG signal data.
 74. Theapparatus according to claims 68, 69, 70, or 71 further defined as onefor ascertaining the hypnotic state of a patient.
 75. Apparatus forascertaining the cerebral state of a patient, including a stateresulting from the administration of a drug, said apparatus comprising:(a) means for obtaining biopotential signals from the patient, thebiopotential signals containing EEG signal data and EMG signal data, theEMG signal data being primarily of a higher frequency than the EEGsignal data which is primarily of a lower frequency; (b) means foranalyzing a sample of sequential signal data over a frequency range thatis sufficiently wide to include both the EEG and EMG signal data toobtain a measure of the complexity of the signal data; and (c) meansproviding the complexity measure as an indicator of the cerebral stateof the patient.
 76. The apparatus according to claim 75 wherein saidanalyzing means is further defined as using a length of EEG signal dataand using a EMG signal data sample of shorter length than the EEG signaldata sample, and wherein said means updates said indicator a repetitionrate determined by the shorter sample length of the EMG signal data toindicate changes in the cerebral state of the patient.
 77. The apparatusaccording to claim 75 or claim 76 wherein said analyzing means isfurther defined as analyzing the signal data over a frequency rangeextending from a frequency of about 0.5 Hz to a frequency which is above32 Hz.
 78. The apparatus according to claim 75 or claim 76 wherein saidanalyzing means is further defined as obtaining a measure of the entropyof the signal data.
 79. The apparatus according to claim 77 wherein saidanalyzing means is further defined as obtaining a measure of the entropyof the signal data.
 80. The apparatus according to claim 75 or claim 76wherein said analyzing means is further defined as obtaining aLempel-Ziv complexity measure of the signal data.
 81. The apparatusaccording to claim 75 or claim 76 further defined as one forascertaining the hypnotic state of a patient.
 82. The apparatusaccording to claim 75 or claim 76 wherein said analyzing means furtherdefined as analyzing a sample of the EEG signal data to obtain acomplexity measure of the EEG signal data and said providing means isfurther defined as providing the complexity measure of the EEG signaldata as further indicator of the cerebral state of the patient.
 83. Theapparatus according to claim 82 further including means for normalizingthe further indication obtained from the analysis of the EEG signal dataand the EEG-EMG signal data indication so that the further indicationand EEG-EMG indication are equal in the absence of EMG signal data. 84.The apparatus according to claim 68 or claim 75 further defined asincluding means notch filtering the signal data to remove power linefrequency harmonics.
 85. The apparatus according to claim 68 or claim 75further defined as including means for processing the signal data todetect artifacts.
 86. The apparatus according to claim 85 furtherdefined as including means for filtering the signal data to removeartifacts.